Classification of Pulse Waveforms Using Edit Distance with Real Penalty
Classification of Pulse Waveforms Using Edit Distance with Real Penalty
复制标题
使用具有实际惩罚的编辑距离对脉冲波形进行分类
DOI：
10.1155/2010/303140
复制
发表时间：
2010-01-01
影响因子：
1.9
通讯作者：
Li, Naimin
中科院分区：
文献类型：
--
作者：
Zhang, Dongyu;Zuo, Wangmeng;Li, Naimin
传感器和信号处理技术的进步为中医脉诊（TCPD）的定量研究提供了有效工具。由于脉象不可避免地存在类内差异，脉搏波形的自动分类一直是一个难题。在本文中，参考带实际惩罚的编辑距离（ERP）以及k - 近邻（KNN）分类器的最新进展，我们提出了两种基于ERP的新型KNN分类器。利用ERP的度量性质，我们首先开发了一个由ERP诱导的内积和一个高斯ERP核，然后将它们嵌入到差异加权KNN分类器中，最终开发出两种用于脉搏波形分类的新型分类器。实验结果表明，所提出的分类器对于脉搏波形的准确分类是有效的。
Advances in sensor and signal processing techniques have provided effective tools for quantitative research in traditional Chinese pulse diagnosis (TCPD). Because of the inevitable intraclass variation of pulse patterns, the automatic classification of pulse waveforms has remained a difficult problem. In this paper, by referring to the edit distance with real penalty (ERP) and the recent progress in k-nearest neighbors (KNN) classifiers, we propose two novel ERP-based KNN classifiers. Taking advantage of the metric property of ERP, we first develop an ERP-induced inner product and a Gaussian ERP kernel, then embed them into difference-weighted KNN classifiers, and finally develop two novel classifiers for pulse waveform classification. The experimental results show that the proposed classifiers are effective for accurate classification of pulse waveform.